EAGER: Towards A Lightweight and Personalized Implicit Authentication System with Adaptive Sensing

基于硬生物识别的身份验证方法，如人脸识别、触摸ID和虹膜扫描，需要用户明确的行动和昂贵的硬件。相比之下，行为是适用于隐式且经济高效的用户身份验证的软生物特征。这种身份验证方法可以降低用户绕过错误设计的身份验证的风险，从而提高安全性。它还减轻了用户记忆密码的负担，革命性地增强了用户体验。然而，行为可能会随着年龄、情绪和环境等许多因素而改变，这一事实使得开发依赖于行为的系统变得更具挑战性。本研究项目致力于设计第一个自动选择动态活动集进行用户行为提取的隐式认证系统，并构建了一个原型认证系统。为了确保实际部署，该系统集成了轻量级计算和自适应感知，使其对常见的能源约束设备具有能效。拟议研究的关键贡献和独特方面之一是，它为仍处于初级阶段的尖端工程系统奠定了基础，其复杂性远远超出了几个新算法。基于行为的隐式认证系统最大的阻碍因素是处理动态行为变化的困难，这使得系统不可靠。这个项目采取了一种截然不同的方法，从可用行为中选择随时间推移最合适的行为集，因此是一个与设备无关的通用框架。将开展三项研究任务：基于概率模型对每个人独特的动态变化行为进行建模和选择；通过解决区分合法用户和非法用户的行为偏差等挑战性问题，开发节能和实用的客户端；构建原型认证系统，以处理认证失败和增强用户体验。这项拟议的研究具有潜在的变革性。探索行为、活动模式和习惯是动态、智能、多功能、无缝和用户友好设计的基础，将现代计算机系统和应用程序转变为我们更智能的助手、教育工作者、健康看护者、安全保险库、艺人、玩伴等。该项目开发了可行的方法来捕获用户的演化行为，动态调整模型，抽象系统组件，使通用系统具有很好的可重用性和可扩展性。研究成果将有助于推进最先进的研究，并推动隐式身份验证系统在网络安全、医疗保健和健身、移动社交网络和电子商务等各个领域的广泛采用。

Hard biometrics-based authentication methods such as face recognition, touch ID and iris scan require explicit action from users and expensive hardware. In contrast, behaviors are soft biometrics suitable for implicit and cost-efficient user authentication. This authentication method can lead to improved security by reducing the risk of users bypassing ill-designed authentication. It also relieves users from the burden of memorizing passwords, revolutionarily enhancing user experience. However, the fact that behaviors can change with many factors such as age, mood, and environment renders it more challenging to develop systems that depend on behaviors. This research project focuses on designing the first implicit authentication system that automatically selects dynamic sets of activities for user behavior extraction, and building a prototype authentication system. To ensure practical deployment, lightweight computation and adaptive sensing are integrated into the system, making it energy-efficient for popular energy-constraint devices.The proposed research project is an exploratory project in its early stages. One of the key contributions and unique aspects of the proposed research is that it sets the foundation for a cutting-edge engineering system that is still in its infancy and whose complexity extends far beyond a few novel algorithms. The most hindering factor of behavior-based implicit authentication systems is the difficulties in handling dynamic behavior changes that make the system unreliable. This project takes a drastically different approach by selecting the most suitable set of behaviors over time from available behaviors, and is thus a general framework that is device-independent. Three research tasks will be carried out: Modeling and selecting the dynamically changing behaviors unique for each person based on probabilistic models; Developing an energy-efficient and practical client side by solving challenging issues such as differentiating legitimate users behavior deviation from illegitimate users; Building a prototype authentication system to handle authentication failures and enhance user experience. The proposed research is potentially transformative. Exploring behaviors, activity patterns and habits serves as the basis for dynamic, intelligent, versatile, seamless and user-friendly designs, transforming modern computer systems and applications into our smarter assistants, educators, health watchers, security vaults, entertainers, play mates and many more. The proposed project has developed viable methods to capture users evolving behaviors, dynamically adjust the models, and abstract system components so that the generic system can be easily reusable and extensible. The research findings will contribute to advancing the state-of-the-art research, and stimulating the wide adoption of implicit authentication systems in various domains including cybersecurity, healthcare and fitness, mobile social networks, and e-commerce.